Training the generator in a GAN pair with back propagation
Let's take a look at the images that fooled the D-network. When this happens the binary cross entropy loss of the D-network is important to look at. The network said a fake image was real. As usual, you back propagate to the very beginning of the D-network. What is the back prop doing? It is telling the weights in the D-network to change such that you lessen the loss (remember you are minimizing the loss function by going down hill --> using gradient descent). So you changed the weights of the D-network, but you really don't have to stop there. 

The complex nonlinear function the D-network is calculating is really f(Image, weights of D-network). So the output of the D-network is really dependent on the Image fed in and the weights of the D-network. The cost is a function of the output f. So we have c = g(f(Image, weights of D-network)) where g is really the binary cross entropy. Looking at this, we see that the cost is also a function of the image!

We can take the partials with respect to the image just like we take the partials with respect to the weights to update the weights. And so after doing back prop to the image, we don't stop -> we take the partial of the cost function with respect to the image that is fed into the D-network and we update the image. And essentially we don't have the original image when we continue to back prop to the beginning of the G-network. Let me know if you'd like more clarification. Again this is my understanding and I haven't coded a GAN yet, so someone please confirm. Also, I am open to others' interpretations.